The logging-while drilling (LWD) borehole images are generated from the azimuthal measurements provided by the LWD tools as the tools rotate inside the borehole during the drilling process. The image measurements are normally binned into a number (denoted as “N”) of azimuthal sectors; with the value of each sector corresponding to the tool reading at an azimuth direction, and the total N sectors covers the 360-degree full azimuth of the borehole. The sector measurements are obtained periodically (e.g., increments of five, ten, or twenty seconds etc.) and the measurements during one time interval are collectively referred to as one scan, which is an N-bin waveform. The LWD borehole image is a collection of such scans. The image may be presented in depth indices by merging and converting the time-indexed scans to the depth domain.
The borehole images contain abundant geological structural information and the drilling induced borehole condition information. The information include, formation beddings, well trajectory dip angles, faults, tool standoffs, wash-out, corkscrew borehole, natural fractures or drilling induced stress or fractures, etc. Such information is used for the driller to make well-placement decision in real-time. Transmission of the images in real-time using the mud-pulse telemetry is challenging due to limited telemetry speed, such as 1.0 to 12.0 bps and limited bandwidth allocation to image transmission, such as less than 2.0 bps. In deeper wells such as the extended reach drilling (ERD) services, the mud pulse signal becomes weaker resulting in even slower telemetry speed (1.0˜3.0 bps or lower). Accordingly, large-scale compression is required. The existing image compression algorithms currently used by the imaging tools such as EcoScope, geoVision Resistivity (GVR), and Azimuthal Density Neutron adnVision tools are two types of the JPEG-style two-dimensional (2D) discrete cosine transform (DCT) based compression. The images produced by the EcoScope and the ADN tools are 16-bin low azimuth-resolution image including density image, gamma-ray image, PE (photo-electric factor) image, density and ultrasonic caliper images. For such images the existing JPEG algorithm used by the EcoScope and ADN tools is less efficient because of a number of reasons described below.
1) The existing JPEG algorithm is 2-D compression requiring a 160-second image block to be transmitted in 160 seconds by eighteen 8-bit transmission data packages (commonly referred to as DPOINTs). If the user places more than eighteen DPOINTs for 160 seconds, the image will not be ready for transmission at the 19th DPOINT and therefore, the bandwidth will be wasted by sending hand-shaking signals. If the user places less than eighteen DPOINTs for 160 seconds, transmission is not able to catch up to the image acquisition speed and as the result, gaps will be observed between two compression blocks. This is not user friendly.
2) The existing JPEG algorithm does not allow the user to adjust recovered image quality based on particular data quality requirement, or the rate of penetration (ROP).
3) The information rate of the existing JPEG algorithm is low. Among the eighteen DPOINTs, seven DPOINTs are overhead used for hand-shaking or error correction. The information rate is 61%.
4) The new version of the JPEG compression, referred to as the multi-mode image compression used by the GVR4 (MicroScope) lateral resistivity image uses a large amount of design and implementation effort and tremendous downhole tool computation resource (e.g., memory, CPU, etc).